Pytest is a powerful Python testing framework that is widely used for unit testing, integration testing, and functional testing. This guide will take you step by step from basic to advanced features of Pytest, providing a comprehensive understanding of its capabilities.

1. Introduction to Pytest
2. Setting Up Pytest

Some notes on Audio pitch correction (eg. autotune, melodyne, etc).

• Melodyne
• FLStudio NewTone
• Synchronarts RePitch

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

A robust, system-friendly guide to resolving SVP 4 failing to detect mpv or initialize motion interpolation on modern Linux distributions (e.g., Arch Linux with Python 3.14+ and VapourSynth R75+).

Disclaimer: Modifying system files in /usr/lib/ is a critical anti-pattern. This guide uses dynamic scripting and user-space (~/.local/) monkey-patching to ensure your fixes survive system updates (pacman -Syu).

curl -fsSL https://ollama.com/install.sh | sh

ollama pull glm-4.7-flash # or gpt-oss:20b (for better performance)

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
  -m"<optional body>" \
  -m"<optional footer>"